Classification and localization
library(keras)
library(rjson)
library(magick)Linking to ImageMagick 6.9.9.38
Enabled features: cairo, fontconfig, freetype, fftw, ghostscript, lcms, pango, rsvg, webp, x11
Disabled features: library(purrr)
library(tibble)
library(tidyr)
library(dplyr)
Attaching package: ‘dplyr’
The following objects are masked from ‘package:stats’:
filter, lag
The following objects are masked from ‘package:base’:
intersect, setdiff, setequal, unionlibrary(ggplot2)Use suppressPackageStartupMessages() to eliminate package startup messages.library(stringr)
use_session_with_seed(7777, disable_gpu = FALSE, disable_parallel_cpu = FALSE)2018-11-29 20:59:49.291996: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:993] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero
2018-11-29 20:59:49.292783: I tensorflow/compiler/xla/service/service.cc:149] XLA service 0x558445b9ca70 executing computations on platform CUDA. Devices:
2018-11-29 20:59:49.292817: I tensorflow/compiler/xla/service/service.cc:157] StreamExecutor device (0): Quadro M2200, Compute Capability 5.2
2018-11-29 20:59:49.293009: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1432] Found device 0 with properties:
name: Quadro M2200 major: 5 minor: 2 memoryClockRate(GHz): 1.036
pciBusID: 0000:01:00.0
totalMemory: 3.94GiB freeMemory: 3.75GiB
2018-11-29 20:59:49.293020: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1511] Adding visible gpu devices: 0
2018-11-29 20:59:49.571337: I tensorflow/core/common_runtime/gpu/gpu_device.cc:982] Device interconnect StreamExecutor with strength 1 edge matrix:
2018-11-29 20:59:49.571361: I tensorflow/core/common_runtime/gpu/gpu_device.cc:988] 0
2018-11-29 20:59:49.571367: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1001] 0: N
2018-11-29 20:59:49.571495: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 3478 MB memory) -> physical GPU (device: 0, name: Quadro M2200, pci bus id: 0000:01:00.0, compute capability: 5.2)
Set session seed to 7777Dataset
We’ll be using images and annotations from the Pascal VOC dataset. The images are here
img_dir <- "data/VOCdevkit/VOC2007/JPEGImages"and the annotations are here
annot_file <- "data/pascal_train2007.json"Preprocessing
Now, the annotations contain information about three types of things we’re interested in.
annotations <- fromJSON(file = annot_file)
str(annotations, max.level = 1)List of 4
$ images :List of 2501
$ type : chr "instances"
$ annotations:List of 7844
$ categories :List of 20First, we need the characteristics of the image itself (height and width) and where it’s stored. Not surprisingly, here it’s one entry per image.
imageinfo <- annotations$images %>% {
tibble(
id = map_dbl(., "id"),
file_name = map_chr(., "file_name"),
image_height = map_dbl(., "height"),
image_width = map_dbl(., "width")
)
}
imageinfoThen, object class ids and bounding box coordinates. There may be multiple of these per image.
boxinfo <- annotations$annotations %>% {
tibble(
image_id = map_dbl(., "image_id"),
category_id = map_dbl(., "category_id"),
bbox = map(., "bbox")
)
}
boxinfoThe bounding boxes are now stored in a list column and need to be unpacked.
boxinfo <- boxinfo %>%
mutate(bbox = unlist(map(.$bbox, function(x) paste(x, collapse = " "))))
boxinfo <- boxinfo %>%
separate(bbox, into = c("x_left", "y_top", "bbox_width", "bbox_height"))
boxinfo <- boxinfo %>% mutate_all(as.numeric)
boxinfoNow we have x_left and y_top coordinates, as well as width and height. We will mostly be working with corner coordinates, so we create the missing x_right and y_top.
As usual in image processing, the y axis starts from the top.
boxinfo <- boxinfo %>%
mutate(y_bottom = y_top + bbox_height - 1, x_right = x_left + bbox_width - 1)
boxinfoFinally, we still need to match class ids to class names.
catinfo <- annotations$categories %>% {
tibble(id = map_dbl(., "id"), name = map_chr(., "name"))
}
catinfoStore a shortcut:
classes <- catinfo$nameSo, putting it all together:
imageinfo <- imageinfo %>%
inner_join(boxinfo, by = c("id" = "image_id")) %>%
inner_join(catinfo, by = c("category_id" = "id"))
imageinfoNote that here still, we have several entries per image, each annotated object occupying its own row.
Soon, we’ll run these images through a model that takes a resolution of 224x224. The bounding box coordinates need to be re-scaled to that size.
target_height <- 224
target_width <- 224
imageinfo <- imageinfo %>% mutate(
x_left_scaled = (x_left / image_width * target_width) %>% round(),
x_right_scaled = (x_right / image_width * target_width) %>% round(),
y_top_scaled = (y_top / image_height * target_height) %>% round(),
y_bottom_scaled = (y_bottom / image_height * target_height) %>% round(),
bbox_width_scaled = (bbox_width / image_width * target_width) %>% round(),
bbox_height_scaled = (bbox_height / image_height * target_height) %>% round()
)
imageinfoWe’re ready. Let’s take a glance at our data. Picking one of the early entries and displaying the original image together with the object annotation yields
img_data <- imageinfo[4,]
img <- image_read(file.path(img_dir, img_data$file_name))
img <- image_draw(img)
rect(
img_data$x_left,
img_data$y_bottom,
img_data$x_right,
img_data$y_top,
border = "white",
lwd = 2
)
text(
img_data$x_left,
img_data$y_top,
img_data$name,
offset = 1,
pos = 2,
cex = 1.5,
col = "white"
)
dev.off()null device
1 Zooming in on one object
We’ll first do classification and localization of a single object, but as this is a multi-object dataset, we need to zoom in on one. A reasonable strategy seems to be choosing the object with the largest ground truth bounding box.
Note: We should be prepared that performance will be worse than had we chosen a single-object dataset, because this dataset does contain multiple salient objects in an image.
imageinfo <- imageinfo %>% mutate(area = bbox_width_scaled * bbox_height_scaled)
imageinfo_maxbb <- imageinfo %>%
group_by(id) %>%
filter(which.max(area) == row_number())
imageinfo_maxbbTrain-test split
Time for action! Split the dataset (imageinfo_maxbb) into 80% training and 20% validation.
n_samples <- nrow(imageinfo_maxbb)
train_indices <- sample(1:n_samples, 0.8 * n_samples)
train_data <- imageinfo_maxbb[train_indices,]
validation_data <- imageinfo_maxbb[-train_indices,]We start with classification and then look at localization.
Single-object classification
Data streaming
Here’s a simple generator that delivers images as well as the corresponding targets in a stream. Later we’ll adapt this to more complex tasks.
Note how the targets are not one-hot-encoded, but integers - keras can handle provided we use the appropriate loss function later.
batch_size <- 2
# this preprocessing function will be called from the generator
load_and_preprocess_image <- function(image_name, target_height, target_width) {
img_array <- image_load(
file.path(img_dir, image_name),
target_size = c(target_height, target_width)
) %>%
image_to_array() %>%
xception_preprocess_input()
dim(img_array) <- c(1, dim(img_array))
img_array
}
# generator function
# can deliver both shuffled as well as non-shuffled data
# returns images and targets (classes) in a list
classification_generator <-
function(data,
target_height,
target_width,
shuffle,
batch_size) {
i <- 1
function() {
if (shuffle) {
indices <- sample(1:nrow(data), size = batch_size)
} else {
if (i + batch_size >= nrow(data))
i <<- 1
indices <- c(i:min(i + batch_size - 1, nrow(data)))
i <<- i + length(indices)
}
x <-
array(0, dim = c(length(indices), target_height, target_width, 3))
y <- array(0, dim = c(length(indices), 1))
for (j in 1:length(indices)) {
x[j, , , ] <-
load_and_preprocess_image(data[[indices[j], "file_name"]],
target_height, target_width)
y[j, ] <-
data[[indices[j], "category_id"]] - 1
}
x <- x / 255
list(x, y)
}
}
# training generator
train_gen <- classification_generator(
train_data,
target_height = target_height,
target_width = target_width,
shuffle = TRUE,
batch_size = batch_size
)
# validation generator
valid_gen <- classification_generator(
validation_data,
target_height = target_height,
target_width = target_width,
shuffle = FALSE,
batch_size = batch_size
)Single-object classification model
Here’s the model for single-object classification.
feature_extractor <-
application_xception(
include_top = FALSE,
input_shape = c(224, 224, 3),
pooling = NULL
)Using TensorFlow backend.
WARNING:tensorflow:From /home/key/anaconda3/envs/tf-master-1111/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py:263: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.
Instructions for updating:
Colocations handled automatically by placer.feature_extractor %>% freeze_weights()
model <- keras_model_sequential() %>%
feature_extractor %>%
layer_flatten() %>%
layer_batch_normalization() %>%
layer_dropout(rate = 0.25) %>%
layer_dense(units = 512, activation = "relu") %>%
layer_batch_normalization() %>%
layer_dropout(rate = 0.25) %>%
layer_dense(units = 20, activation = "softmax")model %>% compile(
optimizer = "adam",
loss = "sparse_categorical_crossentropy",
metrics = list("accuracy")
)Training and predictions
We will skip actually training the model as this would take too long. Instead please go on to the first exercise in 2_objectdetection_quizzes.Rmd.
Here, however, is the training code for further reference.
model %>% fit_generator(
train_gen,
epochs = 20,
steps_per_epoch = nrow(train_data) / batch_size,
validation_data = valid_gen,
validation_steps = nrow(validation_data) / batch_size,
callbacks = list(
callback_early_stopping(patience = 2)
)
)Same as above, here is the code for prediction for further reference.
model %>% predict(load_and_preprocess_image(
train_data$file_name[1],
target_height, target_width)
)Exercise: Multiple object classification
Now go to 2_objectdetection_quizzes.Rmd and look at quiz 1 please.
On to working with a single object again, and a new task: localization.
Single-object localization
Model
Let’s start with the model this time, so it’s clear what the generator has to deliver.
First, code the feature extractor, and freeze its weights. We use Xception, as before. However, there’s an important difference here: We want to keep all spatial information from the before-last layer, so don’t do any pooling or flattening.
feature_extractor <- application_xception(
include_top = FALSE,
input_shape = c(224, 224, 3)
)
feature_extractor %>% freeze_weights()Now we append a custom head. At the output layer, you want to have 4 units, one for each bounding box coordinate.
model <- keras_model_sequential() %>%
feature_extractor %>%
layer_flatten() %>%
layer_batch_normalization() %>%
layer_dropout(rate = 0.25) %>%
layer_dense(units = 512, activation = "relu") %>%
layer_batch_normalization() %>%
layer_dropout(rate = 0.5) %>%
layer_dense(units = 4)Metrics
For the cost function, choose a loss common in regression. In addition, we’d like to keep track of (= use as a custom metric) a more tangible quantity: How much do estimate and ground truth overlap?
Overlap is usually measured as Intersection over Union, or Jaccard distance. Intersection over Union is exactly what it says, a ratio between space shared by the objects and space occupied when we take them together.
Here is an implementation. Use this as an additional metric when compiling the model (hint: wrap it in custom_metric):
metric_iou <- function(y_true, y_pred) {
# order is [x_left, y_top, x_right, y_bottom]
intersection_xmin <- k_maximum(y_true[ ,1], y_pred[ ,1])
intersection_ymin <- k_maximum(y_true[ ,2], y_pred[ ,2])
intersection_xmax <- k_minimum(y_true[ ,3], y_pred[ ,3])
intersection_ymax <- k_minimum(y_true[ ,4], y_pred[ ,4])
area_intersection <- (intersection_xmax - intersection_xmin) *
(intersection_ymax - intersection_ymin)
area_y <- (y_true[ ,3] - y_true[ ,1]) * (y_true[ ,4] - y_true[ ,2])
area_yhat <- (y_pred[ ,3] - y_pred[ ,1]) * (y_pred[ ,4] - y_pred[ ,2])
area_union <- area_y + area_yhat - area_intersection
iou <- area_intersection/area_union
k_mean(iou)
}Now compile the model.
model %>% compile(
optimizer = "adam",
loss = "mae",
metrics = list(custom_metric("iou", metric_iou))
)Data generator
Now we have to modify the generator to return bounding box coordinates as targets…
localization_generator <-
function(data,
target_height,
target_width,
shuffle,
batch_size) {
i <- 1
function() {
if (shuffle) {
indices <- sample(1:nrow(data), size = batch_size)
} else {
if (i + batch_size >= nrow(data))
i <<- 1
indices <- c(i:min(i + batch_size - 1, nrow(data)))
i <<- i + length(indices)
}
x <-
array(0, dim = c(length(indices), target_height, target_width, 3))
y <- array(0, dim = c(length(indices), 4))
for (j in 1:length(indices)) {
x[j, , , ] <-
load_and_preprocess_image(data[[indices[j], "file_name"]],
target_height, target_width)
y[j, ] <-
data[indices[j], c("x_left_scaled",
"y_top_scaled",
"x_right_scaled",
"y_bottom_scaled")] %>% as.matrix()
}
x <- x / 255
list(x, y)
}
}
train_gen <- localization_generator(
train_data,
target_height = target_height,
target_width = target_width,
shuffle = TRUE,
batch_size = batch_size
)
valid_gen <- localization_generator(
validation_data,
target_height = target_height,
target_width = target_width,
shuffle = FALSE,
batch_size = batch_size
)Train the model
This time, we’ll want to train for a short time, say 5 epochs, so we have fun and can draw some bouding boxes.
Start training now, and let’s proceed to the next task /exercise.
model %>% fit_generator(
train_gen,
epochs = 5,
steps_per_epoch = nrow(train_data) / batch_size,
validation_data = valid_gen,
validation_steps = nrow(validation_data) / batch_size,
callbacks = list(
callback_early_stopping(patience = 2)
)
)Epoch 1/5
1/1000 [..............................] - ETA: 1:30:35 - loss: 123.8341 - iou: -0.0794
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1000/1000 [==============================] - 267s 267ms/step - loss: 82.1210 - iou: -0.2045 - val_loss: 33.9797 - val_iou: 0.3819
Epoch 2/5
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1000/1000 [==============================] - 257s 257ms/step - loss: 34.6720 - iou: 0.3806 - val_loss: 33.9237 - val_iou: 0.3895
Epoch 3/5
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92/1000 [=>............................] - ETA: 3:28 - loss: 32.9883 - iou: 0.3986
93/1000 [=>............................] - ETA: 3:27 - loss: 33.0780 - iou: 0.3969
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100/1000 [==>...........................] - ETA: 3:25 - loss: 33.3105 - iou: 0.3931
101/1000 [==>...........................] - ETA: 3:25 - loss: 33.2518 - iou: 0.3941
102/1000 [==>...........................] - ETA: 3:25 - loss: 33.1926 - iou: 0.3948
103/1000 [==>...........................] - ETA: 3:24 - loss: 33.3569 - iou: 0.3939
104/1000 [==>...........................] - ETA: 3:24 - loss: 33.2532 - iou: 0.3954
105/1000 [==>...........................] - ETA: 3:24 - loss: 33.1620 - iou: 0.3969
106/1000 [==>...........................] - ETA: 3:24 - loss: 33.1071 - iou: 0.3971
107/1000 [==>...........................] - ETA: 3:24 - loss: 33.0109 - iou: 0.3990
108/1000 [==>...........................] - ETA: 3:23 - loss: 32.9550 - iou: 0.3999
109/1000 [==>...........................] - ETA: 3:23 - loss: 33.0693 - iou: 0.3988
110/1000 [==>...........................] - ETA: 3:23 - loss: 33.3491 - iou: 0.3956
111/1000 [==>...........................] - ETA: 3:23 - loss: 33.3686 - iou: 0.3953
112/1000 [==>...........................] - ETA: 3:23 - loss: 33.3799 - iou: 0.3954
113/1000 [==>...........................] - ETA: 3:23 - loss: 33.4233 - iou: 0.3944
114/1000 [==>...........................] - ETA: 3:23 - loss: 33.3225 - iou: 0.3952
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116/1000 [==>...........................] - ETA: 3:22 - loss: 33.1931 - iou: 0.3974
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119/1000 [==>...........................] - ETA: 3:21 - loss: 33.3535 - iou: 0.3958
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121/1000 [==>...........................] - ETA: 3:21 - loss: 33.5310 - iou: 0.3937
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126/1000 [==>...........................] - ETA: 3:19 - loss: 33.7897 - iou: 0.3918
127/1000 [==>...........................] - ETA: 3:19 - loss: 33.6287 - iou: 0.3944
128/1000 [==>...........................] - ETA: 3:19 - loss: 33.6731 - iou: 0.3941
129/1000 [==>...........................] - ETA: 3:19 - loss: 33.7191 - iou: 0.3935
130/1000 [==>...........................] - ETA: 3:18 - loss: 33.8031 - iou: 0.3930
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136/1000 [===>..........................] - ETA: 3:17 - loss: 33.6584 - iou: 0.3952
137/1000 [===>..........................] - ETA: 3:17 - loss: 33.7785 - iou: 0.3931
138/1000 [===>..........................] - ETA: 3:17 - loss: 33.7413 - iou: 0.3936
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152/1000 [===>..........................] - ETA: 3:14 - loss: 33.7935 - iou: 0.3927
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157/1000 [===>..........................] - ETA: 3:12 - loss: 33.7907 - iou: 0.3927
158/1000 [===>..........................] - ETA: 3:12 - loss: 33.8474 - iou: 0.3920
159/1000 [===>..........................] - ETA: 3:12 - loss: 33.9595 - iou: 0.3904
160/1000 [===>..........................] - ETA: 3:12 - loss: 33.9704 - iou: 0.3900
161/1000 [===>..........................] - ETA: 3:11 - loss: 33.9243 - iou: 0.3907
162/1000 [===>..........................] - ETA: 3:11 - loss: 33.9535 - iou: 0.3900
163/1000 [===>..........................] - ETA: 3:11 - loss: 34.0082 - iou: 0.3895
164/1000 [===>..........................] - ETA: 3:11 - loss: 34.1992 - iou: 0.3873
165/1000 [===>..........................] - ETA: 3:10 - loss: 34.1268 - iou: 0.3884
166/1000 [===>..........................] - ETA: 3:10 - loss: 34.1634 - iou: 0.3884
167/1000 [====>.........................] - ETA: 3:10 - loss: 34.1910 - iou: 0.3883
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169/1000 [====>.........................] - ETA: 3:09 - loss: 34.2054 - iou: 0.3886
170/1000 [====>.........................] - ETA: 3:09 - loss: 34.1338 - iou: 0.3899
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174/1000 [====>.........................] - ETA: 3:08 - loss: 34.5176 - iou: 0.3855
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177/1000 [====>.........................] - ETA: 3:08 - loss: 34.3958 - iou: 0.3867
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179/1000 [====>.........................] - ETA: 3:07 - loss: 34.3667 - iou: 0.3880
180/1000 [====>.........................] - ETA: 3:07 - loss: 34.3222 - iou: 0.3885
181/1000 [====>.........................] - ETA: 3:07 - loss: 34.3448 - iou: 0.3880
182/1000 [====>.........................] - ETA: 3:06 - loss: 34.2784 - iou: 0.3889
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184/1000 [====>.........................] - ETA: 3:06 - loss: 34.2481 - iou: 0.3897
185/1000 [====>.........................] - ETA: 3:05 - loss: 34.2268 - iou: 0.3900
186/1000 [====>.........................] - ETA: 3:05 - loss: 34.1784 - iou: 0.3900
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189/1000 [====>.........................] - ETA: 3:05 - loss: 34.1547 - iou: 0.3896
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191/1000 [====>.........................] - ETA: 3:04 - loss: 34.1335 - iou: 0.3886
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193/1000 [====>.........................] - ETA: 3:04 - loss: 34.0077 - iou: 0.3908
194/1000 [====>.........................] - ETA: 3:04 - loss: 33.9622 - iou: 0.3916
195/1000 [====>.........................] - ETA: 3:03 - loss: 34.0391 - iou: 0.3904
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197/1000 [====>.........................] - ETA: 3:03 - loss: 34.0038 - iou: 0.3910
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203/1000 [=====>........................] - ETA: 3:02 - loss: 33.9077 - iou: 0.3926
204/1000 [=====>........................] - ETA: 3:01 - loss: 33.9479 - iou: 0.3924
205/1000 [=====>........................] - ETA: 3:02 - loss: 34.0808 - iou: 0.3907
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207/1000 [=====>........................] - ETA: 3:01 - loss: 34.0035 - iou: 0.3913
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214/1000 [=====>........................] - ETA: 2:59 - loss: 33.9788 - iou: 0.3921
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222/1000 [=====>........................] - ETA: 2:58 - loss: 34.1008 - iou: 0.3906
223/1000 [=====>........................] - ETA: 2:57 - loss: 34.1339 - iou: 0.3899
224/1000 [=====>........................] - ETA: 2:57 - loss: 34.1594 - iou: 0.3895
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227/1000 [=====>........................] - ETA: 2:57 - loss: 33.9379 - iou: 0.3924
228/1000 [=====>........................] - ETA: 2:57 - loss: 33.9410 - iou: 0.3923
229/1000 [=====>........................] - ETA: 2:56 - loss: 33.9973 - iou: 0.3915
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232/1000 [=====>........................] - ETA: 2:56 - loss: 34.0213 - iou: 0.3907
233/1000 [=====>........................] - ETA: 2:55 - loss: 33.9261 - iou: 0.3920
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249/1000 [======>.......................] - ETA: 2:52 - loss: 34.0194 - iou: 0.3912
250/1000 [======>.......................] - ETA: 2:52 - loss: 34.1201 - iou: 0.3900
251/1000 [======>.......................] - ETA: 2:51 - loss: 34.1457 - iou: 0.3893
252/1000 [======>.......................] - ETA: 2:51 - loss: 34.1614 - iou: 0.3891
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254/1000 [======>.......................] - ETA: 2:51 - loss: 34.1332 - iou: 0.3894
255/1000 [======>.......................] - ETA: 2:50 - loss: 34.1045 - iou: 0.3898
256/1000 [======>.......................] - ETA: 2:50 - loss: 34.1044 - iou: 0.3897
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258/1000 [======>.......................] - ETA: 2:50 - loss: 34.0755 - iou: 0.3902
259/1000 [======>.......................] - ETA: 2:50 - loss: 34.0708 - iou: 0.3900
260/1000 [======>.......................] - ETA: 2:49 - loss: 34.0666 - iou: 0.3899
261/1000 [======>.......................] - ETA: 2:49 - loss: 34.0750 - iou: 0.3897
262/1000 [======>.......................] - ETA: 2:49 - loss: 34.1440 - iou: 0.3892
263/1000 [======>.......................] - ETA: 2:49 - loss: 34.1449 - iou: 0.3891
264/1000 [======>.......................] - ETA: 2:49 - loss: 34.1696 - iou: 0.3887
265/1000 [======>.......................] - ETA: 2:48 - loss: 34.1687 - iou: 0.3886
266/1000 [======>.......................] - ETA: 2:48 - loss: 34.1670 - iou: 0.3885
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268/1000 [=======>......................] - ETA: 2:48 - loss: 34.1391 - iou: 0.3888
269/1000 [=======>......................] - ETA: 2:47 - loss: 34.1546 - iou: 0.3880
270/1000 [=======>......................] - ETA: 2:47 - loss: 34.1347 - iou: 0.3879
271/1000 [=======>......................] - ETA: 2:47 - loss: 34.1501 - iou: 0.3873
272/1000 [=======>......................] - ETA: 2:47 - loss: 34.1642 - iou: 0.3872
273/1000 [=======>......................] - ETA: 2:46 - loss: 34.2452 - iou: 0.3869
274/1000 [=======>......................] - ETA: 2:46 - loss: 34.2495 - iou: 0.3865
275/1000 [=======>......................] - ETA: 2:46 - loss: 34.2126 - iou: 0.3872
276/1000 [=======>......................] - ETA: 2:46 - loss: 34.1219 - iou: 0.3887
277/1000 [=======>......................] - ETA: 2:46 - loss: 34.0932 - iou: 0.3889
278/1000 [=======>......................] - ETA: 2:46 - loss: 34.0936 - iou: 0.3892
279/1000 [=======>......................] - ETA: 2:45 - loss: 34.0568 - iou: 0.3897
280/1000 [=======>......................] - ETA: 2:45 - loss: 34.0298 - iou: 0.3902
281/1000 [=======>......................] - ETA: 2:45 - loss: 34.0567 - iou: 0.3897
282/1000 [=======>......................] - ETA: 2:45 - loss: 34.1301 - iou: 0.3887
283/1000 [=======>......................] - ETA: 2:44 - loss: 34.1368 - iou: 0.3882
284/1000 [=======>......................] - ETA: 2:44 - loss: 34.2136 - iou: 0.3876
285/1000 [=======>......................] - ETA: 2:44 - loss: 34.3059 - iou: 0.3864
286/1000 [=======>......................] - ETA: 2:44 - loss: 34.3374 - iou: 0.3856
287/1000 [=======>......................] - ETA: 2:43 - loss: 34.2613 - iou: 0.3867
288/1000 [=======>......................] - ETA: 2:43 - loss: 34.3584 - iou: 0.3857
289/1000 [=======>......................] - ETA: 2:43 - loss: 34.3443 - iou: 0.3857
290/1000 [=======>......................] - ETA: 2:43 - loss: 34.3644 - iou: 0.3856
291/1000 [=======>......................] - ETA: 2:43 - loss: 34.3738 - iou: 0.3855
292/1000 [=======>......................] - ETA: 2:42 - loss: 34.3720 - iou: 0.3858
293/1000 [=======>......................] - ETA: 2:42 - loss: 34.3265 - iou: 0.3866
294/1000 [=======>......................] - ETA: 2:42 - loss: 34.3152 - iou: 0.3867
295/1000 [=======>......................] - ETA: 2:42 - loss: 34.2989 - iou: 0.3869
296/1000 [=======>......................] - ETA: 2:41 - loss: 34.2496 - iou: 0.3877
297/1000 [=======>......................] - ETA: 2:41 - loss: 34.2412 - iou: 0.3877
298/1000 [=======>......................] - ETA: 2:41 - loss: 34.2701 - iou: 0.3872
299/1000 [=======>......................] - ETA: 2:41 - loss: 34.2858 - iou: 0.3872
300/1000 [========>.....................] - ETA: 2:41 - loss: 34.2966 - iou: 0.3872
301/1000 [========>.....................] - ETA: 2:41 - loss: 34.3525 - iou: 0.3868
302/1000 [========>.....................] - ETA: 2:40 - loss: 34.3641 - iou: 0.3866
303/1000 [========>.....................] - ETA: 2:40 - loss: 34.3480 - iou: 0.3866
304/1000 [========>.....................] - ETA: 2:40 - loss: 34.3423 - iou: 0.3868
305/1000 [========>.....................] - ETA: 2:40 - loss: 34.3499 - iou: 0.3867
306/1000 [========>.....................] - ETA: 2:39 - loss: 34.3840 - iou: 0.3862
307/1000 [========>.....................] - ETA: 2:39 - loss: 34.4063 - iou: 0.3857
308/1000 [========>.....................] - ETA: 2:39 - loss: 34.4154 - iou: 0.3855
309/1000 [========>.....................] - ETA: 2:39 - loss: 34.3904 - iou: 0.3859
310/1000 [========>.....................] - ETA: 2:39 - loss: 34.4342 - iou: 0.3850
311/1000 [========>.....................] - ETA: 2:38 - loss: 34.4037 - iou: 0.3855
312/1000 [========>.....................] - ETA: 2:38 - loss: 34.3791 - iou: 0.3857
313/1000 [========>.....................] - ETA: 2:38 - loss: 34.3782 - iou: 0.3856Verify some predictions
Here’s a convenience function that displays an image, the ground truth box of the most salient object (as defined above), and if given, class and bounding box predictions.
plot_image_with_boxes <- function(file_name,
object_class,
box,
scaled = FALSE,
class_pred = NULL,
box_pred = NULL) {
img <- image_read(file.path(img_dir, file_name))
if(scaled) img <- image_resize(img, geometry = "224x224!")
img <- image_draw(img)
x_left <- box[1]
y_bottom <- box[2]
x_right <- box[3]
y_top <- box[4]
rect(
x_left,
y_bottom,
x_right,
y_top,
border = "cyan",
lwd = 2.5
)
text(
x_left,
y_top,
object_class,
offset = 1,
pos = 2,
cex = 1.5,
col = "cyan"
)
if (!is.null(box_pred))
rect(box_pred[1],
box_pred[2],
box_pred[3],
box_pred[4],
border = "yellow",
lwd = 2.5)
if (!is.null(class_pred))
text(
box_pred[1],
box_pred[2],
class_pred,
offset = 0,
pos = 4,
cex = 1.5,
col = "yellow")
dev.off()
img %>% image_write(paste0("preds_", file_name))
plot(img)
}Now look at some predictions from the training set. Given you’ve not been training the model for so long, don’t expect too much.
train_1_8 <- train_data[1:8, c("file_name",
"name",
"x_left_scaled",
"y_top_scaled",
"x_right_scaled",
"y_bottom_scaled")]
for (i in 1:8) {
preds <-
model %>% predict(
load_and_preprocess_image(train_1_8[i, "file_name"],
target_height, target_width),
batch_size = 1
)
plot_image_with_boxes(train_1_8$file_name[i],
train_1_8$name[i],
train_1_8[i, 3:6] %>% as.matrix(),
scaled = TRUE,
box_pred = preds)
}2018-11-29 21:29:20.221774: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.05GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2018-11-29 21:29:20.298672: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 2.07GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2018-11-29 21:29:20.304855: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.51GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2018-11-29 21:29:20.390210: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 1.60GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
2018-11-29 21:29:20.401587: W tensorflow/core/common_runtime/bfc_allocator.cc:211] Allocator (GPU_0_bfc) ran out of memory trying to allocate 3.38GiB. The caller indicates that this is not a failure, but may mean that there could be performance gains if more memory were available.
Here are some predictions from a prior training. Looking at the “wrong” predictions, do you have an idea what’s going on?
Single-object detection (classification + localization)
Now we think how we can obtain class prediction and bounding box at the same time.
Before we look at the code, please go to the quizzes and look at quiz 2, which will ask about the basic approach.
Model for single-object detection
Combining regression and classification into one means we’ll want to have two outputs in our model. We’ll thus use the functional API this time.
Again, we start with XCeption as a feature extractor.
rr feature_extractor <- # tbd
Then, we’ll want a common custom head but at the end, two outputs, one for the class prediction and one for the bounding box regression.
rr input <- feature_extractor\(input common <- feature_extractor\)output %>% # tbd
Then in compile, the losses will have to be a list, like so. You’d also want to experiment with weighting losses differently.
rr model %>% freeze_weights(to = )
model %>% compile(# tbd
To be able to train, we’ll also need to adapt the data generator. But we’ll stop here and move on to (multiple) object detection.
---
title: "Classification and localization"
output:
  html_notebook:
    df_print: paged
editor_options: 
  chunk_output_type: inline
---

```{r}
library(keras)
library(rjson)
library(magick)
library(purrr)
library(tibble)
library(tidyr)
library(dplyr)
library(ggplot2)
library(stringr)

use_session_with_seed(7777, disable_gpu = FALSE, disable_parallel_cpu = FALSE)
```


## Dataset

We'll be using images and annotations from the _Pascal VOC dataset_.
The images are here


```{r}
img_dir <- "data/VOCdevkit/VOC2007/JPEGImages"
```

and the annotations are here

```{r}
annot_file <- "data/pascal_train2007.json"
```


## Preprocessing

Now, the annotations contain information about three types of things we're interested in.

```{r}
annotations <- fromJSON(file = annot_file)
str(annotations, max.level = 1)
```


First, we need the characteristics of the image itself (height and width) and where it's stored. Not surprisingly, here it's one entry per image.

```{r}
imageinfo <- annotations$images %>% {
  tibble(
    id = map_dbl(., "id"),
    file_name = map_chr(., "file_name"),
    image_height = map_dbl(., "height"),
    image_width = map_dbl(., "width")
  )
}
imageinfo
```


Then, object class ids and bounding box coordinates. There may be multiple of these per image.


```{r}
boxinfo <- annotations$annotations %>% {
  tibble(
    image_id = map_dbl(., "image_id"),
    category_id = map_dbl(., "category_id"),
    bbox = map(., "bbox")
  )
}
boxinfo
```

The bounding boxes are now stored in a list column and need to be unpacked.

```{r}
boxinfo <- boxinfo %>% 
  mutate(bbox = unlist(map(.$bbox, function(x) paste(x, collapse = " "))))
boxinfo <- boxinfo %>% 
  separate(bbox, into = c("x_left", "y_top", "bbox_width", "bbox_height"))
boxinfo <- boxinfo %>% mutate_all(as.numeric)
boxinfo
```

Now we have `x_left` and `y_top` coordinates, as well as width and height.
We will mostly be working with corner coordinates, so we create the missing `x_right` and `y_top`.

As usual in image processing, the `y` axis starts from the top.

```{r}
boxinfo <- boxinfo %>% 
  mutate(y_bottom = y_top + bbox_height - 1, x_right = x_left + bbox_width - 1)
boxinfo
```


Finally, we still need to match class ids to class names.

```{r}
catinfo <- annotations$categories %>%  {
  tibble(id = map_dbl(., "id"), name = map_chr(., "name"))
}
catinfo
```

Store a shortcut:

```{r}
classes <- catinfo$name
```


So, putting it all together:

```{r}
imageinfo <- imageinfo %>%
  inner_join(boxinfo, by = c("id" = "image_id")) %>%
  inner_join(catinfo, by = c("category_id" = "id"))
imageinfo
```

Note that here still, we have several entries per image, each annotated object occupying its own row.

Soon, we'll run these images through a model that takes a resolution of `224x224`. The bounding box coordinates need to be re-scaled to that size.

```{r}
target_height <- 224
target_width <- 224

imageinfo <- imageinfo %>% mutate(
  x_left_scaled = (x_left / image_width * target_width) %>% round(),
  x_right_scaled = (x_right / image_width * target_width) %>% round(),
  y_top_scaled = (y_top / image_height * target_height) %>% round(),
  y_bottom_scaled = (y_bottom / image_height * target_height) %>% round(),
  bbox_width_scaled =  (bbox_width / image_width * target_width) %>% round(),
  bbox_height_scaled = (bbox_height / image_height * target_height) %>% round()
)
imageinfo
```

We're ready. Let's take a glance at our data. Picking one of the early entries and displaying the original image together with the object annotation yields

```{r}
img_data <- imageinfo[4,]
img <- image_read(file.path(img_dir, img_data$file_name))
img <- image_draw(img)
rect(
  img_data$x_left,
  img_data$y_bottom,
  img_data$x_right,
  img_data$y_top,
  border = "white",
  lwd = 2
)
text(
  img_data$x_left,
  img_data$y_top,
  img_data$name,
  offset = 1,
  pos = 2,
  cex = 1.5,
  col = "white"
)
dev.off()
```


## Zooming in on one object


We'll first do classification and localization of a single object, but as this is a multi-object dataset, we need to zoom in on one. A reasonable strategy seems to be choosing the object with the largest ground truth bounding box.

Note: We should be prepared that performance will be worse than had we chosen a single-object dataset, because this dataset _does_ contain multiple salient objects in an image.

```{r}
imageinfo <- imageinfo %>% mutate(area = bbox_width_scaled * bbox_height_scaled)

imageinfo_maxbb <- imageinfo %>%
  group_by(id) %>%
  filter(which.max(area) == row_number())

imageinfo_maxbb
```


## Train-test split

Time for action! Split the dataset (`imageinfo_maxbb`) into 80% training and 20% validation.

```{r}
n_samples <- nrow(imageinfo_maxbb)
train_indices <- sample(1:n_samples, 0.8 * n_samples)
train_data <- imageinfo_maxbb[train_indices,]
validation_data <- imageinfo_maxbb[-train_indices,]
```

We start with classification and then look at localization.

## Single-object classification

### Data streaming

Here's a simple generator that delivers images as well as the corresponding targets in a stream. 
Later we'll adapt this to more complex tasks.

Note how the targets are not one-hot-encoded, but integers - `keras` can handle provided we use the appropriate loss function later.


```{r}
batch_size <- 2

# this preprocessing function will be called from the generator
load_and_preprocess_image <- function(image_name, target_height, target_width) {
  img_array <- image_load(
    file.path(img_dir, image_name),
    target_size = c(target_height, target_width)
    ) %>%
    image_to_array() %>%
    xception_preprocess_input() 
  dim(img_array) <- c(1, dim(img_array))
  img_array
}

# generator function
# can deliver both shuffled as well as non-shuffled data
# returns images and targets (classes) in a list
classification_generator <-
  function(data,
           target_height,
           target_width,
           shuffle,
           batch_size) {
    i <- 1
    function() {
      if (shuffle) {
        indices <- sample(1:nrow(data), size = batch_size)
      } else {
        if (i + batch_size >= nrow(data))
          i <<- 1
        indices <- c(i:min(i + batch_size - 1, nrow(data)))
        i <<- i + length(indices)
      }
      x <-
        array(0, dim = c(length(indices), target_height, target_width, 3))
      y <- array(0, dim = c(length(indices), 1))
      
      for (j in 1:length(indices)) {
        x[j, , , ] <-
          load_and_preprocess_image(data[[indices[j], "file_name"]],
                                    target_height, target_width)
        y[j, ] <-
          data[[indices[j], "category_id"]] - 1
      }
      x <- x / 255
      list(x, y)
    }
  }

# training generator
train_gen <- classification_generator(
  train_data,
  target_height = target_height,
  target_width = target_width,
  shuffle = TRUE,
  batch_size = batch_size
)

# validation generator
valid_gen <- classification_generator(
  validation_data,
  target_height = target_height,
  target_width = target_width,
  shuffle = FALSE,
  batch_size = batch_size
)
```


# Single-object classification model

Here's the model for single-object classification. 

```{r}
feature_extractor <-
  application_xception(
    include_top = FALSE,
    input_shape = c(224, 224, 3),
    pooling = NULL
)

feature_extractor %>% freeze_weights()

model <- keras_model_sequential() %>%
  feature_extractor %>%
  layer_flatten() %>%
  layer_batch_normalization() %>%
  layer_dropout(rate = 0.25) %>%
  layer_dense(units = 512, activation = "relu") %>%
  layer_batch_normalization() %>%
  layer_dropout(rate = 0.25) %>%
  layer_dense(units = 20, activation = "softmax")
```


```{r}
model %>% compile(
  optimizer = "adam",
  loss = "sparse_categorical_crossentropy",
  metrics = list("accuracy")
)
```


##### Training and predictions

We will skip actually training the model as this would take too long.
Instead please go on to the first exercise in [2_objectdetection_quizzes.Rmd](2_objectdetection_quizzes.Rmd).

Here, however, is the training code for further reference.


```{r, eval=FALSE}
model %>% fit_generator(
  train_gen,
  epochs = 20,
  steps_per_epoch = nrow(train_data) / batch_size,
  validation_data = valid_gen,
  validation_steps = nrow(validation_data) / batch_size,
  callbacks = list(
    callback_early_stopping(patience = 2)
  )
)
```

Same as above, here is the code for prediction for further reference.

```{r, eval=FALSE}
model %>% predict(load_and_preprocess_image(
  train_data$file_name[1],
  target_height, target_width)
)
```


### Exercise: Multiple object classification

Now go to [2_objectdetection_quizzes.Rmd](2_objectdetection_quizzes.Rmd) and look at quiz 1 please.


On to working with a single object again, and a new task: localization.

## Single-object localization

### Model

Let's start with the model this time, so it's clear what the generator has to deliver. 

First, code the feature extractor, and freeze its weights. We use Xception, as before.
However, there's an important difference here: We want to keep all spatial information from the before-last layer, so don't do any pooling or flattening.


```{r}
feature_extractor <- application_xception(
  include_top = FALSE,
  input_shape = c(224, 224, 3)
)

feature_extractor %>% freeze_weights()
```


Now we append a custom head. At the output layer, you want to have 4 units, one for each bounding box coordinate.

```{r}
model <- keras_model_sequential() %>%
  feature_extractor %>%
  layer_flatten() %>%
  layer_batch_normalization() %>%
  layer_dropout(rate = 0.25) %>%
  layer_dense(units = 512, activation = "relu") %>%
  layer_batch_normalization() %>%
  layer_dropout(rate = 0.5) %>%
  layer_dense(units = 4)
```


### Metrics

For the cost function, choose a loss common in regression. In addition, we'd like to keep track of (= use as a custom metric) a more tangible quantity: How much do estimate and ground truth overlap?

Overlap is usually measured as _Intersection over Union_, or _Jaccard distance_. Intersection over Union is exactly what it says, a ratio between space shared by the objects and space occupied when we take them together.

Here is an implementation. Use this as an additional metric when compiling the model (hint: wrap it in `custom_metric`):

```{r}
metric_iou <- function(y_true, y_pred) {
  
  # order is [x_left, y_top, x_right, y_bottom]
  intersection_xmin <- k_maximum(y_true[ ,1], y_pred[ ,1])
  intersection_ymin <- k_maximum(y_true[ ,2], y_pred[ ,2])
  intersection_xmax <- k_minimum(y_true[ ,3], y_pred[ ,3])
  intersection_ymax <- k_minimum(y_true[ ,4], y_pred[ ,4])
  
  area_intersection <- (intersection_xmax - intersection_xmin) * 
                       (intersection_ymax - intersection_ymin)
  area_y <- (y_true[ ,3] - y_true[ ,1]) * (y_true[ ,4] - y_true[ ,2])
  area_yhat <- (y_pred[ ,3] - y_pred[ ,1]) * (y_pred[ ,4] - y_pred[ ,2])
  area_union <- area_y + area_yhat - area_intersection
  
  iou <- area_intersection/area_union
  k_mean(iou)
  
}
```

Now compile the model.

```{r}
model %>% compile(
  optimizer = "adam",
  loss = "mae",
  metrics = list(custom_metric("iou", metric_iou))
)
```

### Data generator

Now we have to modify the generator to return bounding box coordinates as targets...

```{r}
localization_generator <-
  function(data,
           target_height,
           target_width,
           shuffle,
           batch_size) {
    i <- 1
    function() {
      if (shuffle) {
        indices <- sample(1:nrow(data), size = batch_size)
      } else {
        if (i + batch_size >= nrow(data))
          i <<- 1
        indices <- c(i:min(i + batch_size - 1, nrow(data)))
        i <<- i + length(indices)
      }
      x <-
        array(0, dim = c(length(indices), target_height, target_width, 3))
      y <- array(0, dim = c(length(indices), 4))
      
      for (j in 1:length(indices)) {
        x[j, , , ] <-
          load_and_preprocess_image(data[[indices[j], "file_name"]], 
                                    target_height, target_width)
        y[j, ] <-
          data[indices[j], c("x_left_scaled",
                             "y_top_scaled",
                             "x_right_scaled",
                             "y_bottom_scaled")] %>% as.matrix()
      }
      x <- x / 255
      list(x, y)
    }
  }

train_gen <- localization_generator(
  train_data,
  target_height = target_height,
  target_width = target_width,
  shuffle = TRUE,
  batch_size = batch_size
)

valid_gen <- localization_generator(
  validation_data,
  target_height = target_height,
  target_width = target_width,
  shuffle = FALSE,
  batch_size = batch_size
)
```

### Train the model

This time, we'll want to train for a short time, say 5 epochs, so we have fun and can draw some bouding boxes. 

Start training now, and let's proceed to the next task /exercise.


```{r}
model %>% fit_generator(
  train_gen,
  epochs = 5,
  steps_per_epoch = nrow(train_data) / batch_size,
  validation_data = valid_gen,
  validation_steps = nrow(validation_data) / batch_size,
  callbacks = list(
   callback_early_stopping(patience = 2)
  )
)
```

### Verify some predictions

Here's a convenience function that displays an image, the ground truth box of the most salient object (as defined above), and if given, class and bounding box predictions.

```{r}
plot_image_with_boxes <- function(file_name,
                                  object_class,
                                  box,
                                  scaled = FALSE,
                                  class_pred = NULL,
                                  box_pred = NULL) {
  img <- image_read(file.path(img_dir, file_name))
  if(scaled) img <- image_resize(img, geometry = "224x224!")
  img <- image_draw(img)
  x_left <- box[1]
  y_bottom <- box[2]
  x_right <- box[3]
  y_top <- box[4]
  rect(
    x_left,
    y_bottom,
    x_right,
    y_top,
    border = "cyan",
    lwd = 2.5
  )
  text(
    x_left,
    y_top,
    object_class,
    offset = 1,
    pos = 2,
    cex = 1.5,
    col = "cyan"
  )
  if (!is.null(box_pred))
    rect(box_pred[1],
         box_pred[2],
         box_pred[3],
         box_pred[4],
         border = "yellow",
         lwd = 2.5)
  if (!is.null(class_pred))
    text(
      box_pred[1],
      box_pred[2],
      class_pred,
      offset = 0,
      pos = 4,
      cex = 1.5,
      col = "yellow")
  dev.off()
  img %>% image_write(paste0("preds_", file_name))
  plot(img)
}
```

Now look at some predictions from the training set. Given you've not been training the model for so long, don't expect too much. 


```{r}
train_1_8 <- train_data[1:8, c("file_name",
                               "name",
                               "x_left_scaled",
                               "y_top_scaled",
                               "x_right_scaled",
                               "y_bottom_scaled")]

for (i in 1:8) {
  preds <-
    model %>% predict(
      load_and_preprocess_image(train_1_8[i, "file_name"], 
                                target_height, target_width),
      batch_size = 1
  )
  plot_image_with_boxes(train_1_8$file_name[i],
                        train_1_8$name[i],
                        train_1_8[i, 3:6] %>% as.matrix(),
                        scaled = TRUE,
                        box_pred = preds)
}
```

Here are some predictions from a prior training. Looking at the "wrong" predictions, do you have an idea what's going on?

![](images/preds_train.jpg){width=100%}



## Single-object detection (classification + localization)

Now we think how we can obtain class prediction and bounding box at the same time. 

Before we look at the code, please go to the quizzes and look at quiz 2, which  will ask about the basic approach.

### Model for single-object detection

Combining regression and classification into one means we'll want to have two outputs in our model.
We'll thus use the functional API this time. 

Again, we start with XCeption as a feature extractor.

```{r}
feature_extractor <- application_xception(
  include_top = FALSE,
  input_shape = c(224, 224, 3)
)
```

Then, we'll want a common custom head but at the end, two outputs, one for the class prediction and one for the bounding box regression.

```{r}
input <- feature_extractor$input
common <- feature_extractor$output %>%
  layer_flatten(name = "flatten") %>%
  layer_activation_relu() %>%
  layer_dropout(rate = 0.25) %>%
  layer_dense(units = 512, activation = "relu") %>%
  layer_batch_normalization() %>%
  layer_dropout(rate = 0.5)

regression_output <-
  layer_dense(common, units = 4, name = "regression_output")
class_output <- layer_dense(
  common,
  units = 20,
  activation = "softmax",
  name = "class_output"
)

model <- keras_model(
  inputs = input,
  outputs = list(regression_output, class_output)
)
```

Then in `compile`, the losses will have to be a list, like so.
You'd also want to experiment with weighting losses differently.

```{r}
model %>% freeze_weights(to = "flatten")

model %>% compile(
  optimizer = "adam",
  loss = list("mae", "sparse_categorical_crossentropy"),
  #loss_weights = list(
  #  regression_output = 0.05,
  #  class_output = 0.95),
  metrics = list(
    regression_output = custom_metric("iou", metric_iou),
    class_output = "accuracy"
  )
)
```

To be able to train, we'll also need to adapt the data generator. But we'll stop here and move on to (multiple) object detection.